Bayesian Inference for Generalized Linear and Proportional Hazards Models Via Gibbs Sampling

Petros Dellaportas and A. F. M. Smith

Journal of the Royal Statistical Society Series C, 1993, vol. 42, issue 3, 443-459

Abstract: It is shown that Gibbs sampling, making systematic use of an adaptive rejection algorithm proposed by Gilks and Wild, provides a straightforward computational procedure for Bayesian inferences in a wide class of generalized linear and proportional hazards models.

Date: 1993
References: Add references at CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
https://doi.org/10.2307/2986324

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:42:y:1993:i:3:p:443-459

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().